In [1]:
#importing Libraries
import pandas as pd
import plotly.express as px
import plotly.io as pi
In [2]:
#Loading Dataset
dataset = pd.read_csv("Zomato_Mumbai_Dataset.csv",delimiter = "|")
dataset
Out[2]:
NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Mumbai First International Financial Centre-- Bandra ... https://www.zomato.com/mumbai/hitchki-bandra-k... 1 Casual Dining 12noon to 130am(Mon-Sun) Excellent 4.9 3529
1 Baba Falooda 400 Desserts,Ice Cream,Beverages Mumbai Mahim https://www.zomato.com/mumbai/baba-falooda-mah... 1 Dessert Parlor 2pm to 1am(Mon-Sun) Very Good 4.4 1723
2 Chin Chin Chu 1800 Asian,Chinese Mumbai Juhu https://www.zomato.com/mumbai/chin-chin-chu-ju... 1 Casual Dining 12noon to 1am(Mon-Sun) Very Good 4.2 337
3 Butterfly High 1000 Modern Indian Mumbai Bandra Kurla Complex https://www.zomato.com/mumbai/butterfly-high-b... 1 Bar 12noon to 130am(Mon-Sun) Very Good 4.3 1200
4 BKC DIVE 1200 North Indian,Chinese,Continental Mumbai Bandra Kurla Complex https://www.zomato.com/mumbai/bkc-dive-bandra-... 1 Bar 1130am to 1am(Mon-Sun) Veľmi dobré 4.4 5995
... ... ... ... ... ... ... ... ... ... ... ... ...
15076 Hari Om Snack Bar 350 Fast Food,South Indian,Chinese Mumbai Kandivali West https://www.zomato.com/mumbai/hari-om-snack-ba... 99 Quick Bites 11am to 230am(Mon-Sun) Good 3.7 64
15077 PitaBurg 400 Fast Food,Lebanese Mumbai Lower Parel https://www.zomato.com/mumbai/pitaburg-lower-p... 99 none 11am to 11pm(Mon,Tue,Wed,Thu,Sun),11am to ... Average 3.4 99
15078 Uncha Otlawala 300 Desserts,Ice Cream Mumbai Kandivali West https://www.zomato.com/mumbai/uncha-otlawala-1... 99 Dessert Parlor 9am to 1230AM(Mon-Sun) Good 3.5 29
15079 Mandarin Panda 400 Desserts,Chinese,Thai Mumbai Malad West https://www.zomato.com/mumbai/mandarin-panda-m... 99 none 12noon to 330pm,7pm to 1am(Mon-Sun) Good 3.7 121
15080  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

15081 rows × 12 columns

In [3]:
dataset.shape
Out[3]:
(15081, 12)
In [4]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15081 entries, 0 to 15080
Data columns (total 12 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   NAME             15081 non-null  object
 1   PRICE            15080 non-null  object
 2   CUSINE_CATEGORY  15079 non-null  object
 3   CITY             15080 non-null  object
 4   REGION           15080 non-null  object
 5   URL              15080 non-null  object
 6   PAGE NO          15080 non-null  object
 7   CUSINE TYPE      15080 non-null  object
 8   TIMING           15015 non-null  object
 9   RATING_TYPE      14070 non-null  object
 10  RATING           15080 non-null  object
 11  VOTES            15080 non-null  object
dtypes: object(12)
memory usage: 1.4+ MB
In [5]:
dataset.describe()
Out[5]:
NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
count 15081 15080 15079 15080 15080 15080 15080 15080 15015 14070 15080 15080
unique 12720 67 3183 2 241 13823 944 23 2551 31 35 1124
top NAME 400 CUSINE_CATEGORY Mumbai REGION URL PAGE NO Quick Bites 11am to 11pm(Mon-Sun) Average - -
freq 942 2042 942 14138 942 942 942 5262 1192 5112 2360 2360
In [6]:
#Checking Null Values
dataset.isnull().sum()
Out[6]:
NAME                  0
PRICE                 1
CUSINE_CATEGORY       2
CITY                  1
REGION                1
URL                   1
PAGE NO               1
CUSINE TYPE           1
TIMING               66
RATING_TYPE        1011
RATING                1
VOTES                 1
dtype: int64
In [7]:
pageNo = dataset["PAGE NO"] == "PAGE NO"
dataset[pageNo]
Out[7]:
NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
15 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
31 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
47 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
63 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
79 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
... ... ... ... ... ... ... ... ... ... ... ... ...
15000 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
15016 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
15032 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
15048 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
15064 NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES

942 rows × 12 columns

In [8]:
dataset = dataset[~pageNo]  
dataset
Out[8]:
NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Mumbai First International Financial Centre-- Bandra ... https://www.zomato.com/mumbai/hitchki-bandra-k... 1 Casual Dining 12noon to 130am(Mon-Sun) Excellent 4.9 3529
1 Baba Falooda 400 Desserts,Ice Cream,Beverages Mumbai Mahim https://www.zomato.com/mumbai/baba-falooda-mah... 1 Dessert Parlor 2pm to 1am(Mon-Sun) Very Good 4.4 1723
2 Chin Chin Chu 1800 Asian,Chinese Mumbai Juhu https://www.zomato.com/mumbai/chin-chin-chu-ju... 1 Casual Dining 12noon to 1am(Mon-Sun) Very Good 4.2 337
3 Butterfly High 1000 Modern Indian Mumbai Bandra Kurla Complex https://www.zomato.com/mumbai/butterfly-high-b... 1 Bar 12noon to 130am(Mon-Sun) Very Good 4.3 1200
4 BKC DIVE 1200 North Indian,Chinese,Continental Mumbai Bandra Kurla Complex https://www.zomato.com/mumbai/bkc-dive-bandra-... 1 Bar 1130am to 1am(Mon-Sun) Veľmi dobré 4.4 5995
... ... ... ... ... ... ... ... ... ... ... ... ...
15076 Hari Om Snack Bar 350 Fast Food,South Indian,Chinese Mumbai Kandivali West https://www.zomato.com/mumbai/hari-om-snack-ba... 99 Quick Bites 11am to 230am(Mon-Sun) Good 3.7 64
15077 PitaBurg 400 Fast Food,Lebanese Mumbai Lower Parel https://www.zomato.com/mumbai/pitaburg-lower-p... 99 none 11am to 11pm(Mon,Tue,Wed,Thu,Sun),11am to ... Average 3.4 99
15078 Uncha Otlawala 300 Desserts,Ice Cream Mumbai Kandivali West https://www.zomato.com/mumbai/uncha-otlawala-1... 99 Dessert Parlor 9am to 1230AM(Mon-Sun) Good 3.5 29
15079 Mandarin Panda 400 Desserts,Chinese,Thai Mumbai Malad West https://www.zomato.com/mumbai/mandarin-panda-m... 99 none 12noon to 330pm,7pm to 1am(Mon-Sun) Good 3.7 121
15080  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

14139 rows × 12 columns

In [9]:
dataset.isnull().sum()
Out[9]:
NAME                  0
PRICE                 1
CUSINE_CATEGORY       2
CITY                  1
REGION                1
URL                   1
PAGE NO               1
CUSINE TYPE           1
TIMING               66
RATING_TYPE        1011
RATING                1
VOTES                 1
dtype: int64
In [10]:
dataset[dataset["PRICE"].isnull()]
Out[10]:
NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
15080  NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
In [11]:
dataset = dataset.drop(labels= 15080,axis = 0)
In [12]:
dataset[dataset["PRICE"].isnull()]
Out[12]:
NAME PRICE CUSINE_CATEGORY CITY REGION URL PAGE NO CUSINE TYPE TIMING RATING_TYPE RATING VOTES
In [13]:
#Replacing Null Values
dataset.fillna("NA",inplace = True)
In [14]:
dataset.isnull().sum()
Out[14]:
NAME               0
PRICE              0
CUSINE_CATEGORY    0
CITY               0
REGION             0
URL                0
PAGE NO            0
CUSINE TYPE        0
TIMING             0
RATING_TYPE        0
RATING             0
VOTES              0
dtype: int64
In [15]:
dataset["RATING"].value_counts()
Out[15]:
RATING
-          2360
3.5        1094
3.4        1036
3.6         960
NEW         953
3.3         926
3.7         917
3.2         801
3.8         782
3.1         734
3.0         622
3.9         596
2.9         409
4.0         408
2.8         309
4.1         298
4.2         199
2.7         170
4.3         148
4.4          99
2.6          77
Opening      57
4.5          46
2.5          39
4.6          32
2.4          26
4.7          13
2.3          10
2.1           5
2.2           4
4.8           4
4.9           2
1.8           1
2.0           1
Name: count, dtype: int64
In [16]:
dataset["RATING"].replace(to_replace = ["Opening","-","NEW"], value = '0', inplace = True)

C:\Users\swdeveloper.AD\AppData\Local\Temp\ipykernel_5384\4096954408.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  dataset["RATING"].replace(to_replace = ["Opening","-","NEW"], value = '0', inplace = True)
In [17]:
dataset["RATING"].value_counts()
Out[17]:
RATING
0      3370
3.5    1094
3.4    1036
3.6     960
3.3     926
3.7     917
3.2     801
3.8     782
3.1     734
3.0     622
3.9     596
2.9     409
4.0     408
2.8     309
4.1     298
4.2     199
2.7     170
4.3     148
4.4      99
2.6      77
4.5      46
2.5      39
4.6      32
2.4      26
4.7      13
2.3      10
2.1       5
2.2       4
4.8       4
4.9       2
1.8       1
2.0       1
Name: count, dtype: int64
In [18]:
dataset["VOTES"].value_counts()
Out[18]:
VOTES
-       2360
NEW      953
4        364
5        320
6        288
        ... 
1029       1
7350       1
964        1
585        1
1249       1
Name: count, Length: 1123, dtype: int64
In [19]:
dataset["VOTES"].replace(to_replace = ["-","NEW","Opening"], value = '0', inplace = True)

C:\Users\swdeveloper.AD\AppData\Local\Temp\ipykernel_5384\694549743.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  dataset["VOTES"].replace(to_replace = ["-","NEW","Opening"], value = '0', inplace = True)
In [20]:
dataset["VOTES"].value_counts()
Out[20]:
VOTES
0       3371
4        364
5        320
6        288
7        277
        ... 
1029       1
7350       1
964        1
585        1
1249       1
Name: count, Length: 1120, dtype: int64
In [21]:
#Converting to Int,Float Datatype
dataset["PRICE"] = dataset["PRICE"].astype("int64")
dataset["RATING"] = dataset["RATING"].astype("float64")
dataset["VOTES"] = dataset["VOTES"].astype("int64")
In [22]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
Index: 14138 entries, 0 to 15079
Data columns (total 12 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   NAME             14138 non-null  object 
 1   PRICE            14138 non-null  int64  
 2   CUSINE_CATEGORY  14138 non-null  object 
 3   CITY             14138 non-null  object 
 4   REGION           14138 non-null  object 
 5   URL              14138 non-null  object 
 6   PAGE NO          14138 non-null  object 
 7   CUSINE TYPE      14138 non-null  object 
 8   TIMING           14138 non-null  object 
 9   RATING_TYPE      14138 non-null  object 
 10  RATING           14138 non-null  float64
 11  VOTES            14138 non-null  int64  
dtypes: float64(1), int64(2), object(9)
memory usage: 1.4+ MB
In [23]:
dataset["TIMING"].value_counts()
Out[23]:
TIMING
11am to 11pm(Mon-Sun)                              1192
11am to 12midnight(Mon-Sun)                         632
12noon to 12midnight(Mon-Sun)                       467
11am to 1130pm(Mon-Sun)                             309
10am to 10pm(Mon-Sun)                               267
                                                   ... 
1130am to 4pm,630pm to 1230AM...                      1
12midnight to 5am,12noon to 12midnight(Mon-Sun)       1
12midnight to 1230AM,12noon to 4pm,7pm to ...         1
12noon to 330pm,630pm to 12midnight...                1
8am to 11pm,12midnight to 115am(Mon-Sun)              1
Name: count, Length: 2551, dtype: int64
In [24]:
tempDf = dataset["TIMING"].str.split("(", n = 1, expand = True)
tempDf
Out[24]:
0 1
0 12noon to 130am Mon-Sun)
1 2pm to 1am Mon-Sun)
2 12noon to 1am Mon-Sun)
3 12noon to 130am Mon-Sun)
4 1130am to 1am Mon-Sun)
... ... ...
15075 8am to 11pm,12midnight to 115am Mon-Sun)
15076 11am to 230am Mon-Sun)
15077 11am to 11pm Mon,Tue,Wed,Thu,Sun),11am to ...
15078 9am to 1230AM Mon-Sun)
15079 12noon to 330pm,7pm to 1am Mon-Sun)

14138 rows × 2 columns

In [25]:
dataset["TIMING"] = tempDf[0]
dataset["DAYS_OPEN"] = tempDf[1]
In [26]:
dataset.drop(['URL', 'PAGE NO', 'CITY'],axis = 1, inplace = True)
In [27]:
dataset.head()
Out[27]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... First International Financial Centre-- Bandra ... Casual Dining 12noon to 130am Excellent 4.9 3529 Mon-Sun)
1 Baba Falooda 400 Desserts,Ice Cream,Beverages Mahim Dessert Parlor 2pm to 1am Very Good 4.4 1723 Mon-Sun)
2 Chin Chin Chu 1800 Asian,Chinese Juhu Casual Dining 12noon to 1am Very Good 4.2 337 Mon-Sun)
3 Butterfly High 1000 Modern Indian Bandra Kurla Complex Bar 12noon to 130am Very Good 4.3 1200 Mon-Sun)
4 BKC DIVE 1200 North Indian,Chinese,Continental Bandra Kurla Complex Bar 1130am to 1am Veľmi dobré 4.4 5995 Mon-Sun)
In [28]:
dataset['DAYS_OPEN'] = dataset['DAYS_OPEN'].str.replace(r'\)', '', regex=True)
dataset.head()
Out[28]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... First International Financial Centre-- Bandra ... Casual Dining 12noon to 130am Excellent 4.9 3529 Mon-Sun
1 Baba Falooda 400 Desserts,Ice Cream,Beverages Mahim Dessert Parlor 2pm to 1am Very Good 4.4 1723 Mon-Sun
2 Chin Chin Chu 1800 Asian,Chinese Juhu Casual Dining 12noon to 1am Very Good 4.2 337 Mon-Sun
3 Butterfly High 1000 Modern Indian Bandra Kurla Complex Bar 12noon to 130am Very Good 4.3 1200 Mon-Sun
4 BKC DIVE 1200 North Indian,Chinese,Continental Bandra Kurla Complex Bar 1130am to 1am Veľmi dobré 4.4 5995 Mon-Sun
In [29]:
dataset.isnull().sum()
Out[29]:
NAME                 0
PRICE                0
CUSINE_CATEGORY      0
REGION               0
CUSINE TYPE          0
TIMING               0
RATING_TYPE          0
RATING               0
VOTES                0
DAYS_OPEN          160
dtype: int64
In [30]:
dataset.fillna('NA',inplace = True)
In [31]:
dataset.isnull().sum()
Out[31]:
NAME               0
PRICE              0
CUSINE_CATEGORY    0
REGION             0
CUSINE TYPE        0
TIMING             0
RATING_TYPE        0
RATING             0
VOTES              0
DAYS_OPEN          0
dtype: int64
In [32]:
dataset.info()

<class 'pandas.core.frame.DataFrame'>
Index: 14138 entries, 0 to 15079
Data columns (total 10 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   NAME             14138 non-null  object 
 1   PRICE            14138 non-null  int64  
 2   CUSINE_CATEGORY  14138 non-null  object 
 3   REGION           14138 non-null  object 
 4   CUSINE TYPE      14138 non-null  object 
 5   TIMING           14138 non-null  object 
 6   RATING_TYPE      14138 non-null  object 
 7   RATING           14138 non-null  float64
 8   VOTES            14138 non-null  int64  
 9   DAYS_OPEN        14138 non-null  object 
dtypes: float64(1), int64(2), object(7)
memory usage: 1.2+ MB
In [33]:
dataset  = dataset[~((dataset['RATING'] == 0.0) | (dataset['VOTES'] == 0))]
dataset
Out[33]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... First International Financial Centre-- Bandra ... Casual Dining 12noon to 130am Excellent 4.9 3529 Mon-Sun
1 Baba Falooda 400 Desserts,Ice Cream,Beverages Mahim Dessert Parlor 2pm to 1am Very Good 4.4 1723 Mon-Sun
2 Chin Chin Chu 1800 Asian,Chinese Juhu Casual Dining 12noon to 1am Very Good 4.2 337 Mon-Sun
3 Butterfly High 1000 Modern Indian Bandra Kurla Complex Bar 12noon to 130am Very Good 4.3 1200 Mon-Sun
4 BKC DIVE 1200 North Indian,Chinese,Continental Bandra Kurla Complex Bar 1130am to 1am Veľmi dobré 4.4 5995 Mon-Sun
... ... ... ... ... ... ... ... ... ... ...
15075 Tirupati Balaji 500 Chinese,Fast Food,North Indian Oshiwara-- Andheri West Casual Dining 8am to 11pm,12midnight to 115am Good 3.5 267 Mon-Sun
15076 Hari Om Snack Bar 350 Fast Food,South Indian,Chinese Kandivali West Quick Bites 11am to 230am Good 3.7 64 Mon-Sun
15077 PitaBurg 400 Fast Food,Lebanese Lower Parel none 11am to 11pm Average 3.4 99 Mon,Tue,Wed,Thu,Sun,11am to ...
15078 Uncha Otlawala 300 Desserts,Ice Cream Kandivali West Dessert Parlor 9am to 1230AM Good 3.5 29 Mon-Sun
15079 Mandarin Panda 400 Desserts,Chinese,Thai Malad West none 12noon to 330pm,7pm to 1am Good 3.7 121 Mon-Sun

10767 rows × 10 columns

In [34]:
dataset["RATING_TYPE"].value_counts()
Out[34]:
RATING_TYPE
Average          5111
Good             4330
Very Good        1137
Excellent          95
Poor               47
Veľmi dobré         6
Skvělá volba        4
Dobrze              4
Bardzo dobrze       3
Ortalama            2
Bueno               2
İyi                 2
Buono               2
Dobré               2
Bom                 2
Priemer             2
Průměr              2
Muito Bom           2
Promedio            2
Muy Bueno           1
Sangat Baik         1
Média               1
Biasa               1
Skvělé              1
Baik                1
Çok iyi             1
Excelente           1
Velmi dobré         1
Media               1
Name: count, dtype: int64
In [35]:
#Replacing the Values
dataset['RATING_TYPE'].replace(to_replace='Excelente' , value='Excellent', inplace=True)
dataset['RATING_TYPE'].replace(to_replace=['Veľmi dobré','Bardzo dobrze','Muy Bueno','Velmi dobré'] , value='Very Good', inplace=True)
dataset['RATING_TYPE'].replace(to_replace=['Skvělá volba','Dobrze','Bueno','Buono','Dobré','Bom','Skvělé'] , value='Good', inplace=True)
dataset['RATING_TYPE'].replace(to_replace=['Priemer','Média','Çok iyi'] , value='Average', inplace=True)
dataset['RATING_TYPE'].replace(to_replace=['Průměr','Promedio','Ortalama','Muito Bom','İyi'] , value='Poor', inplace=True)
dataset['RATING_TYPE'].replace(to_replace=['Baik','Biasa','Media','Sangat Baik'] , value='Very Poor', inplace=True)
                              

C:\Users\swdeveloper.AD\AppData\Local\Temp\ipykernel_5384\4268798849.py:2: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  dataset['RATING_TYPE'].replace(to_replace='Excelente' , value='Excellent', inplace=True)
C:\Users\swdeveloper.AD\AppData\Local\Temp\ipykernel_5384\4268798849.py:3: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  dataset['RATING_TYPE'].replace(to_replace=['Veľmi dobré','Bardzo dobrze','Muy Bueno','Velmi dobré'] , value='Very Good', inplace=True)
In [36]:
dataset["RATING_TYPE"].value_counts()
Out[36]:
RATING_TYPE
Average      5115
Good         4347
Very Good    1148
Excellent      96
Poor           57
Very Poor       4
Name: count, dtype: int64
In [37]:
dataset["REGION"].value_counts()
Out[37]:
REGION
Mira Road                            405
Malad West                           308
Chembur                              277
Kharghar                             268
Borivali West                        264
                                    ... 
Hotel Emerald-- Juhu                   1
Trident-- Bandra Kurla Complex         1
Sea Princess-- Juhu                    1
Aureole Hotel-- Andheri East           1
Hotel Satkar Residency-- Majiwada      1
Name: count, Length: 237, dtype: int64
In [38]:
dataset["REGION"] = dataset["REGION"].str.replace("[a-zA-Z].+-- ","",regex = True)
In [39]:
dataset["REGION"].value_counts()
Out[39]:
REGION
Thane West           712
Mira Road            412
Andheri West         407
Malad West           316
Bandra West          282
                    ... 
Andheri East           2
CBD Belapur            1
Girgaon Chowpatty      1
Goregaon               1
Dadar                  1
Name: count, Length: 120, dtype: int64
In [40]:
dataset["REGION"] = dataset["REGION"].str.replace("west| West| East| east","",regex = True)
In [41]:
dataset["REGION"].value_counts()
Out[41]:
REGION
Thane                726
Mira Road            412
Andheri              409
Malad                378
Kandivali            377
                    ... 
Flea Bazaar Café       3
Majiwada               3
Panvel                 2
CBD Belapur            1
Girgaon Chowpatty      1
Name: count, Length: 101, dtype: int64
In [42]:
dataset['REGION'] = dataset['REGION'].str.replace('4 Bungalows|7 Andheri|Azad Nagar|Near Andheri Station|Veera Desai Area','Andheri',regex = True)
dataset['REGION'] = dataset['REGION'].str.replace('Bandra Kurla Complex','Bandra',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('CBD-Belapur','CBD Belapur',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Girgaon Chowpatty','Chowpatty',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Dadar Shivaji Park','Dadar',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Flea Bazaar Café|Kamala Mills Compound','Lower Parel',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Runwal Green','Mulund',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Mumbai CST Area','Mumbai Central',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Kopar Khairane|Seawoods|Turbhe|Ulwe','Navi Mumbai',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('New Panvel|Old Panvel','Panvel',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Kamothe','Sion',regex=True)
dataset['REGION'] = dataset['REGION'].str.replace('Ghodbunder Road|Majiwada','Thane',regex=True)
In [43]:
dataset[dataset.duplicated()]
Out[43]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
4064 Sai Sannidhi Restaurant & Bar 1000 North Indian,Konkan Dahisar Casual Dining 11am to 12midnight Good 3.7 99 Mon-Sun
4068 Konkan Katta 400 Seafood,Maharashtrian,Malwani Mahakali Quick Bites 11am to 330pm,630pm to 1130pm Good 3.5 181 Mon-Sun
4082 Usmaniya Hotel 600 Mughlai Fort Casual Dining 1030am to 1130pm Average 3.2 8 Mon-Sun
4083 Gina's Cakes 450 Bakery Dombivali none 11am to 11pm Good 3.5 49 Mon-Sun
4084 Konkanastha Lunch Home 400 Seafood,Malwani Chakala Casual Dining 12noon to 3pm,730pm to 1030pm Good 3.5 44 Mon-Sun
... ... ... ... ... ... ... ... ... ... ...
14200 Mezbaan Family Restaurant 350 Chinese,Mughlai Mumbra Dhaba 12noon to 1230AM Average 2.8 97 Mon-Sun
14204 Jyoti Lunch Home 650 Chinese,North Indian,Seafood,Mughlai Mulund Casual Dining 11am to 1230AM Good 3.5 49 Mon-Sun
14253 On Toes 900 Italian,North Indian,Chinese Malad Casual Dining 12noon to 3pm,7pm to 1230AM Good 3.6 76 Mon-Sun
14761 Frosty Farm 400 Ice Cream,Desserts,Fast Food Malad Dessert Parlor 1pm to 1215AM Good 3.6 120 Mon-Sun
14928 Shree Manu Sagar 300 North Indian,Chinese,Indian Ghansoli Quick Bites 1130am to 415pm,7pm to 1215AM Average 3.3 51 Mon-Sun

220 rows × 10 columns

In [44]:
dataset = dataset.drop_duplicates()
In [45]:
dataset.shape
Out[45]:
(10547, 10)
In [46]:
zomato_dataset = dataset.copy()
In [47]:
zomato_dataset
Out[47]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Bandra Casual Dining 12noon to 130am Excellent 4.9 3529 Mon-Sun
1 Baba Falooda 400 Desserts,Ice Cream,Beverages Mahim Dessert Parlor 2pm to 1am Very Good 4.4 1723 Mon-Sun
2 Chin Chin Chu 1800 Asian,Chinese Juhu Casual Dining 12noon to 1am Very Good 4.2 337 Mon-Sun
3 Butterfly High 1000 Modern Indian Bandra Bar 12noon to 130am Very Good 4.3 1200 Mon-Sun
4 BKC DIVE 1200 North Indian,Chinese,Continental Bandra Bar 1130am to 1am Very Good 4.4 5995 Mon-Sun
... ... ... ... ... ... ... ... ... ... ...
15075 Tirupati Balaji 500 Chinese,Fast Food,North Indian Andheri Casual Dining 8am to 11pm,12midnight to 115am Good 3.5 267 Mon-Sun
15076 Hari Om Snack Bar 350 Fast Food,South Indian,Chinese Kandivali Quick Bites 11am to 230am Good 3.7 64 Mon-Sun
15077 PitaBurg 400 Fast Food,Lebanese Lower Parel none 11am to 11pm Average 3.4 99 Mon,Tue,Wed,Thu,Sun,11am to ...
15078 Uncha Otlawala 300 Desserts,Ice Cream Kandivali Dessert Parlor 9am to 1230AM Good 3.5 29 Mon-Sun
15079 Mandarin Panda 400 Desserts,Chinese,Thai Malad none 12noon to 330pm,7pm to 1am Good 3.7 121 Mon-Sun

10547 rows × 10 columns

Exploratory Data Analysis¶

Q1) How many restaurants are in Mumbai for each type of cuisine?

In [48]:
#pip install -U kaleido
In [49]:
restaurant = px.histogram(zomato_dataset,x = "CUSINE TYPE",color = "CUSINE TYPE",title = "No of Restaurants By Cusine Type in Mumbai",labels = {"CUSINE TYPE" : "Cusine"})
restaurant.show()

Q2) What are the percentage of restaurants by Rating Type in Mumbai?

In [50]:
ratings = zomato_dataset["RATING_TYPE"].value_counts().reset_index()
ratings.rename(columns = {'index' : 'RATING_TYPE','count' : "No of Restaurants"},inplace = True)
ratings
Out[50]:
RATING_TYPE No of Restaurants
0 Average 4983
1 Good 4263
2 Very Good 1145
3 Excellent 96
4 Poor 56
5 Very Poor 4
In [51]:
px.pie(ratings,names = "RATING_TYPE",values = "No of Restaurants",color = "RATING_TYPE", title = "Percentage of Restaurants in Mumbai")

Q3) Which are the Top 10 highest rated Seafood Restaurant in Mumbai?

In [52]:
seafood = zomato_dataset[zomato_dataset["CUSINE_CATEGORY"].str.contains("Seafood") ]
seafood.sort_values(by="RATING",ascending = False).head(10)
Out[52]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
7104 Thangabali 1000 Seafood,South Indian,Mangalorean,Andhra,Kerala Khar Bar 12noon to 4pm,7pm to 3am Excellent 4.7 564 Mon-Sun
76 Ceremonial Kitchen & Co 1000 Seafood,Maharashtrian,North Indian,Chinese Thane Casual Dining 1130am to 1130pm Excellent 4.6 350 Mon-Sun
13685 Maharashtra Lunch Home 600 Maharashtrian,Malwani,Konkan,Seafood Kharghar Casual Dining 11am to 345pm,7pm to 1145pm Excellent 4.6 209 Mon-Sun
12433 Quarter Canteen 1100 North Indian,Seafood,Chinese Bandra Casual Dining 12noon to 330pm,7pm to 1am Excellent 4.5 573 Mon-Sun
902 The Harbour Bay - SeaFood Kitchen & Bar 2400 Seafood,Beverages Bandra Casual Dining 12noon to 1am Excellent 4.5 100 Mon-Sun
884 Rajmanya- Seafood family restaurant 800 Maharashtrian,Konkan,Seafood Vashi Casual Dining 11am to 11pm Excellent 4.5 178 Mon-Sun
3380 Peco Peco 700 Chinese,Seafood,Asian Powai none 12noon to 330pm,7pm to 1230AM Excellent 4.5 497 Mon-Sun
9954 Pi Bar and Kitchen 1600 Continental,European,Italian,Seafood,Pizza,Des... Andheri Bar 12noon to 6pm,7pm to 12midnight Excellent 4.5 2068 Mon-Sun
903 Ferry Wharf 1500 Seafood,Mangalorean Bandra Casual Dining 11am to 330pm,7pm to 1230AM Very Good 4.4 459 Mon-Sun
915 Monis Bar and Restaurant 1000 North Indian,Chinese,Continental,Seafood,Bever... Thane Casual Dining 1130am to 330pm,6pm to 1130pm Very Good 4.4 662 Mon-Sun

Q4) Which is the best Food Truck in Mumbai?

In [53]:
foodTruck = zomato_dataset[zomato_dataset["CUSINE TYPE"].str.contains("Food Truck")]
foodTruck.sort_values(by = "RATING", ascending = False).head(3)
Out[53]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
262 Dumpling Delights 200 Momos Matunga Food Truck 430pm to 930pm Very Good 4.3 212 Mon-Sun
1017 Street Food Co. 250 Fast Food,Chinese Virar Food Truck 6pm to 3am Very Good 4.1 274 Mon-Sun
7922 Honeys Delights 100 Fast Food,Burger Malad Food Truck Closed Good 3.9 66 Mon,4am to 7am(Tue-Sun

Q5) Which places have the highest rated restaurant for each Cuisine Type in Mumbai?

In [54]:
highest_rating = zomato_dataset[zomato_dataset["RATING"] >= 4.5]
highest_rating
Out[54]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Bandra Casual Dining 12noon to 130am Excellent 4.9 3529 Mon-Sun
6 Persian Darbar 1300 Biryani,North Indian,Chinese,Mughlai Marol Casual Dining 10am to 3am Excellent 4.5 3058 Mon-Sun
7 Tanatan 1500 Modern Indian Juhu Casual Dining 12noon to 130am Excellent 4.7 1842 Mon-Sun
9 Plum by Bent Chair 1800 Asian Lower Parel Casual Dining 12noon to 1am Excellent 4.7 1876 Mon-Sun
10 Angrezi Dhaba 1500 North Indian,Chinese,Thai,European Dadar Bar 12noon to 1am Excellent 4.5 2092 Mon-Sun
... ... ... ... ... ... ... ... ... ... ...
14228 Zaika Crave - Club Aquaria 1300 North Indian,Continental,Chinese,Desserts Borivali Casual Dining 11am to 330pm,7pm to 1130pm Excellent 4.5 1302 Mon,Tue,Wed...
14234 Cone Culture 250 European Kharghar Casual Dining Closed Excellent 4.6 492 Mon,12noon to 11pm(Tue-Sun
15007 Dessertino 300 Desserts,Ice Cream Kandivali Dessert Parlor 11am to 12midnight Excellent 4.8 184 Mon-Sun
15051 Tick-eat 800 North Indian,Italian,Chinese,Mexican,Lebanese Mulund Casual Dining 1130am to 330pm,7pm to 1130pm Excellent 4.5 754 Mon-Sun
15056 Daftar Goregaon 750 Pizza,Chinese,North Indian,Beverages Goregaon Casual Dining 12noon to 1130pm Excellent 4.6 388 Mon-Sun

97 rows × 10 columns

In [55]:
px.histogram(highest_rating,x="REGION",color = "CUSINE TYPE" , title = "No of Best Restaurants Region Wise")

Q6) What is the Avg Price Distibution of highest rated restaurant for each Cuisine Type in Mumbai?

In [56]:
avg_price = highest_rating.groupby(by = ["REGION","CUSINE TYPE"])["PRICE"].mean().reset_index()
avg_price.head()
Out[56]:
REGION CUSINE TYPE PRICE
0 Andheri Bar 1600.0
1 Andheri Café 800.0
2 Andheri Casual Dining 1175.0
3 Andheri Dessert Parlor 300.0
4 Andheri Lounge 1700.0
In [57]:
px.scatter(highest_rating,x="REGION",y = "PRICE", color = "CUSINE TYPE", symbol = "CUSINE TYPE",title="Avg Price Distribution").update_traces(marker_size=10)

Q7) Which areas have a large number of Chinese Restaurant Market?

In [58]:
chinese_rest = zomato_dataset[zomato_dataset["CUSINE_CATEGORY"].str.contains("Chinese")]
chinese_rest
Out[58]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Bandra Casual Dining 12noon to 130am Excellent 4.9 3529 Mon-Sun
2 Chin Chin Chu 1800 Asian,Chinese Juhu Casual Dining 12noon to 1am Very Good 4.2 337 Mon-Sun
4 BKC DIVE 1200 North Indian,Chinese,Continental Bandra Bar 1130am to 1am Very Good 4.4 5995 Mon-Sun
5 Flea Bazaar Café 800 American,Asian,Street Food,North Indian,Luckno... Lower Parel Café 12noon to 1am Very Good 4.2 2042 Mon-Sun
6 Persian Darbar 1300 Biryani,North Indian,Chinese,Mughlai Marol Casual Dining 10am to 3am Excellent 4.5 3058 Mon-Sun
... ... ... ... ... ... ... ... ... ... ...
15071 Lucknow Zaika 500 North Indian,Chinese Kurla Quick Bites 12noon to 2am Average 2.6 36 Mon-Sun
15072 Zuha's Kitchen 400 Chinese,North Indian,Mughlai Mumbai Central Quick Bites 12noon to 4pm,730pm to 430am Average 3.3 13 Mon-Sun
15075 Tirupati Balaji 500 Chinese,Fast Food,North Indian Andheri Casual Dining 8am to 11pm,12midnight to 115am Good 3.5 267 Mon-Sun
15076 Hari Om Snack Bar 350 Fast Food,South Indian,Chinese Kandivali Quick Bites 11am to 230am Good 3.7 64 Mon-Sun
15079 Mandarin Panda 400 Desserts,Chinese,Thai Malad none 12noon to 330pm,7pm to 1am Good 3.7 121 Mon-Sun

5119 rows × 10 columns

In [59]:
chinese_r = chinese_rest.groupby(by = "REGION").agg({'NAME':'count','PRICE' : 'mean'})
chinese_r = chinese_r.sort_values('NAME',ascending = False)
chinese_r.head()
Out[59]:
NAME PRICE
REGION
Thane 371 590.983827
Andheri 313 627.316294
Mira Road 224 553.348214
Malad 186 604.032258
Goregaon 163 548.773006

Q8) Is there a relation between Price and Rating by each Cuisine Type?

In [60]:
rating_price = zomato_dataset.groupby(by = ["CUSINE TYPE","RATING"])["PRICE"].mean().reset_index()
rating_price.head()
Out[60]:
CUSINE TYPE RATING PRICE
0 Bakery 2.7 400.000000
1 Bakery 2.8 285.714286
2 Bakery 2.9 328.571429
3 Bakery 3.0 300.000000
4 Bakery 3.1 369.117647
In [61]:
px.line(rating_price,y= "PRICE",x = "RATING",color = "CUSINE TYPE")

Q9) Is there a relation between Region and Price?

In [62]:
region_price = zomato_dataset.groupby("REGION")["PRICE"].mean().reset_index()
region_price.head()
Out[62]:
REGION PRICE
0 Airoli 452.287582
1 Alibaug 665.000000
2 Ambernath 514.000000
3 Andheri 562.506596
4 Bandra 724.945946
In [63]:
px.scatter(region_price,x="REGION",y="PRICE").update_traces(marker_size= 7)

Q10) Find the list of Affordable Restaurants?

In [64]:
affordable_rest = zomato_dataset[["NAME","CUSINE TYPE","REGION","CUSINE_CATEGORY","RATING","PRICE"]]
affordable_rest = affordable_rest[affordable_rest["PRICE"] <= affordable_rest["PRICE"].max() / 4].sort_values(by = "PRICE" ,ascending = False)
affordable_rest
Out[64]:
NAME CUSINE TYPE REGION CUSINE_CATEGORY RATING PRICE
7301 SamBar Pub & Kitchen Pub Khar Finger Food,South Indian,North Indian 3.9 1250
6045 Fabelle at The Chocolate Boutique - ITC Grand ... Dessert Parlor Parel Desserts 3.7 1250
964 Bijoli Grill Casual Dining Powai Bengali 3.8 1250
2068 Young Chilli Casual Dining Vashi Asian,Continental,North Indian 4.1 1200
1882 Cafe at The NCPA Casual Dining Nariman Point American,Parsi,Asian,North Indian 4.0 1200
... ... ... ... ... ... ...
11239 Cakewaali.co Bakery Navi Mumbai Bakery 3.2 100
8199 Gupta's Kulfi Centre none Chowpatty Desserts,Ice Cream 3.6 100
2925 Jab We Eat none Girgaum South Indian,North Indian,Maharashtrian,Fast Food 3.3 50
9598 Ho5 Store none Matunga Fast Food 3.2 50
6137 Sanjog Wine N Dine Casual Dining Thane North Indian,Chinese 3.5 5

10190 rows × 6 columns

In [65]:
highest_rating_rest = zomato_dataset[["NAME","CUSINE TYPE","REGION","CUSINE_CATEGORY","RATING","PRICE"]]
highest_rating_rest = highest_rating_rest[highest_rating_rest["RATING"] >= 4.5]  
highest_rating_rest
Out[65]:
NAME CUSINE TYPE REGION CUSINE_CATEGORY RATING PRICE
0 Hitchki Casual Dining Bandra Modern Indian,North Indian,Chinese,Momos,Birya... 4.9 1200
6 Persian Darbar Casual Dining Marol Biryani,North Indian,Chinese,Mughlai 4.5 1300
7 Tanatan Casual Dining Juhu Modern Indian 4.7 1500
9 Plum by Bent Chair Casual Dining Lower Parel Asian 4.7 1800
10 Angrezi Dhaba Bar Dadar North Indian,Chinese,Thai,European 4.5 1500
... ... ... ... ... ... ...
14228 Zaika Crave - Club Aquaria Casual Dining Borivali North Indian,Continental,Chinese,Desserts 4.5 1300
14234 Cone Culture Casual Dining Kharghar European 4.6 250
15007 Dessertino Dessert Parlor Kandivali Desserts,Ice Cream 4.8 300
15051 Tick-eat Casual Dining Mulund North Indian,Italian,Chinese,Mexican,Lebanese 4.5 800
15056 Daftar Goregaon Casual Dining Goregaon Pizza,Chinese,North Indian,Beverages 4.6 750

97 rows × 6 columns

In [66]:
highest_afford_rest = pd.merge(affordable_rest,highest_rating_rest,how = "inner", on = ["NAME","REGION"])
#highest_afford_rest

highest_afford_rest = highest_afford_rest[['NAME', 'PRICE_x', 'CUSINE_CATEGORY_x', 'REGION', 'CUSINE TYPE_x']]
highest_afford_rest.rename(columns = {'NAME' : 'NAME' ,'PRICE_X' : 'PRICE','CUSINE_CATEGORY_X' : 'CUSINE_CATEGORY','REGION_X' : 'REGION',
                                      'CUSINE_TYPE_X' : 'CUSINE_TYPE'},inplace = True) 
highest_afford_rest
Out[66]:
NAME PRICE_x CUSINE_CATEGORY_x REGION CUSINE TYPE_x
0 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Bandra Casual Dining
1 The Joker Bistro 1200 North Indian,Chinese,Continental CBD Belapur Casual Dining
2 Culinary Tales 1200 Chinese,European,Continental,Salad,Italian,Pizza Andheri Casual Dining
3 Wild Dining Restaurant 1200 North Indian,Continental,Mexican,Chinese Andheri Casual Dining
4 Invento 1200 Chinese,Fast Food,North Indian,Italian,Mexican Lower Parel Casual Dining
... ... ... ... ... ...
60 Belo Pops 300 Ice Cream,Desserts,Beverages Andheri none
61 Cone Culture 250 European Kharghar Casual Dining
62 Curry And Combos Twist 200 North Indian,Chinese Andheri Quick Bites
63 Moussestruck 200 Desserts Andheri none
64 Cake Centre-The Dessert Maker 150 Desserts Mohammad Ali Road Dessert Parlor

65 rows × 5 columns

Q10) Find the list of most Reliable Restaurants?

In [67]:
reliable_rest = zomato_dataset[zomato_dataset["VOTES"] >= zomato_dataset["VOTES"].mean()]
reliable_rest.sort_values(by = "VOTES" , ascending = False,inplace = True)
reliable_rest

C:\Users\swdeveloper.AD\AppData\Local\Temp\ipykernel_5384\2083225331.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[67]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE TIMING RATING_TYPE RATING VOTES DAYS_OPEN
8897 Candies 700 Cafe,Italian,North Indian,Desserts Bandra Café Closed Very Good 4.2 10217 Mon,830am to 10pm(Tue-Sun
3751 Prithvi Cafe 700 Cafe,Fast Food Juhu Café 1030am to 1030pm Very Good 4.4 8000 Mon-Sun
5337 Chili's American Grill & Bar 1400 American,Mexican,Burger,Tex-Mex Powai Casual Dining 12noon to 130am Very Good 4.3 7377 Mon-Sun
1251 Joey's Pizza 800 Pizza Malad Quick Bites 11am to 12midnight Excellent 4.6 7350 Mon-Sun
8539 Leopold Cafe & Bar 1600 American,Chinese,Mughlai,Italian Colaba Casual Dining 730am to 12midnight Good 3.9 7327 Mon-Sun
... ... ... ... ... ... ... ... ... ... ...
3828 Frozen Delight -The Dessert Cafe 250 Desserts,Ice Cream Airoli Dessert Parlor Closed Good 3.9 178 Mon,11am to 12midnight(Tue-Sun
11211 Soul Curry 800 Chinese,North Indian,Malwani,Mughlai,Biryani Bhandup Casual Dining 11am to 1230AM Good 3.7 178 Mon-Sun
2891 New Wanton Point 450 Chinese,North Indian,Seafood Kandivali Casual Dining 11am to 12midnight Good 3.6 178 Mon-Sun
99 Appetite - Resto Bar 700 North Indian,Chinese Powai Casual Dining 12noon to 4pm,630pm to 130am Good 3.7 178 Mon-Sun
12267 Buddha Bowl 1000 Asian,Healthy Food Bandra none 12noon to 330pm,7pm to 1130pm Very Good 4.1 178 Mon-Thu,12noon...

2345 rows × 10 columns

In [68]:
reliable_rest_df = pd.merge(reliable_rest, highest_afford_rest, how='inner', on=['NAME', 'REGION'])
reliable_rest_df = reliable_rest_df[['NAME', 'PRICE_x', 'CUSINE_CATEGORY_x', 'REGION', 'CUSINE TYPE_x']]
reliable_rest_df.rename(columns={'NAME':'NAME', 'PRICE_x':'PRICE', 'CUSINE_CATEGORY_x':'CUSINE_CATEGORY',
 'REGION':'REGION', 'CUSINE TYPE_x':'CUSINE TYPE'},inplace=True)
reliable_rest_df
Out[68]:
NAME PRICE CUSINE_CATEGORY REGION CUSINE TYPE
0 Joey's Pizza 800 Pizza Malad Quick Bites
1 Vedge 1000 Thai,Chinese,North Indian,Mexican,Italian,Asian Andheri Casual Dining
2 Hitchki 1200 Modern Indian,North Indian,Chinese,Momos,Birya... Bandra Casual Dining
3 Cafe Monza 1000 Italian,American,Salad,Mexican Kharghar Casual Dining
4 Bombay Salad Co. 900 Salad,Healthy Food,Juices Bandra Casual Dining
5 Family Tree 800 Italian,Mexican,North Indian,Chinese,Salad Thane Casual Dining
6 Wild Dining Restaurant 1200 North Indian,Continental,Mexican,Chinese Andheri Casual Dining
7 Rajdhani 950 Gujarati,Rajasthani,North Indian Ghatkopar Casual Dining
8 Aquafire Restaurant 1100 North Indian,Continental,Chinese,Italian Vile Parle Casual Dining
9 Spice Republic 1200 Cafe,Continental,Mediterranean,Mexican,Italian... Borivali Café
10 Shaollin Temple 1000 Chinese,Thai CBD Belapur Casual Dining
11 Zaika Restaurant & Party Hall 1000 North Indian,Chinese,Beverages Bhayandar Casual Dining
12 Coppetto Artisan Gelato 350 Ice Cream,Desserts Bandra Dessert Parlor
13 Tick-eat 800 North Indian,Italian,Chinese,Mexican,Lebanese Mulund Casual Dining
14 Belo Pops 300 Ice Cream,Desserts,Beverages Andheri none
15 Curry Culture 800 North Indian,Biryani,Chinese,Kebab,Mughlai,Asian Powai none
16 Dum & Curry 700 Mughlai,North Indian,Chinese Powai Quick Bites
17 Culinary Tales 1200 Chinese,European,Continental,Salad,Italian,Pizza Andheri Casual Dining
18 Quarter Canteen 1100 North Indian,Seafood,Chinese Bandra Casual Dining
19 Harsh's Bistro 800 Chinese,Continental Malad Casual Dining
20 Thangabali 1000 Seafood,South Indian,Mangalorean,Andhra,Kerala Khar Bar
21 Justice Cafe and Dine 800 Cafe,Chinese,Italian,Continental,North Indian,... Thane Café
22 Shuruwat- Veg Food Journey 600 Continental,Tea,South Indian,Fast Food,Pizza,N... Ghatkopar Casual Dining
23 Peco Peco 700 Chinese,Seafood,Asian Powai none
24 Cone Culture 250 European Kharghar Casual Dining
25 The Joker Bistro 1200 North Indian,Chinese,Continental CBD Belapur Casual Dining
26 Makhan Singh 800 North Indian,Chinese,Biryani Powai none
27 Poetry By Love & Cheesecake 1000 Cafe,Desserts Juhu Café
28 Daftar Goregaon 750 Pizza,Chinese,North Indian,Beverages Goregaon Casual Dining
29 Moussestruck 200 Desserts Andheri none
30 Ceremonial Kitchen & Co 1000 Seafood,Maharashtrian,North Indian,Chinese Thane Casual Dining
31 Little West Pizza 600 Pizza Borivali Quick Bites
32 Tossin Pizza 900 Pizza,Italian,Fast Food Chembur Casual Dining
33 Maezo 1000 Modern Indian Thane Casual Dining
34 Angrezi Patiyalaa 1200 North Indian,Finger Food,American,Mexican,Chinese Andheri Casual Dining
35 Sandy's Den 1000 Fast Food,Bar Food Chembur Casual Dining
36 Big Bang Cuurry 350 North Indian,Biryani,Rolls Andheri none
37 Regano's 600 Continental,Fast Food,Italian,Desserts Malad Casual Dining
38 Maharashtra Lunch Home 600 Maharashtrian,Malwani,Konkan,Seafood Kharghar Casual Dining
39 Invento 1200 Chinese,Fast Food,North Indian,Italian,Mexican Lower Parel Casual Dining
40 Dessertino 300 Desserts,Ice Cream Kandivali Dessert Parlor
41 Fresh Food Co. 500 Continental,Healthy Food,Salad,Beverages,Desse... Santacruz none
42 Rajmanya- Seafood family restaurant 800 Maharashtrian,Konkan,Seafood Vashi Casual Dining